LEADER 02642nam 2200505Ia 450 001 9910438057103321 005 20200520144314.0 010 $a3-642-38652-0 024 7 $a10.1007/978-3-642-38652-7 035 $a(OCoLC)847735622 035 $a(MiFhGG)GVRL6WIW 035 $a(CKB)2670000000371295 035 $a(MiAaPQ)EBC1317208 035 $a(EXLCZ)992670000000371295 100 $a20130314d2013 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aDimensionality reduction with unsupervised nearest neighbors /$fOliver Kramer 205 $a1st ed. 2013. 210 $aDordrecht $cSpringer$d2013 215 $a1 online resource (xviii, 130 pages) $cillustrations (some color) 225 0 $aIntelligent systems reference library ;$v51 300 $a"ISSN: 1868-4394." 311 $a3-642-38651-2 320 $aIncludes bibliographical references and index. 327 $aPart I Foundations -- Part II Unsupervised Nearest Neighbors -- Part III Conclusions. 330 $aThis book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.  . 410 0$aIntelligent systems reference library ;$vv. 51. 606 $aDimensions 606 $aData mining 615 0$aDimensions. 615 0$aData mining. 676 $a006.31 676 $a519.5/36 700 $aKramer$b Oliver$0761919 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910438057103321 996 $aDimensionality Reduction with Unsupervised Nearest Neighbors$92513616 997 $aUNINA